Azure Data Engineer with AI

#Azure Data Engineer With AI

Azure Data Engineer is a prominent job role responsible for design of Data Warehouses (DWH). This trending job stream involves Extraction (E) of data from various sources, perform data mashup and Transformations (T) and Loading the data (L) into Warehouse and Lakehouse platforms. With AI & CoPilot we can implement this ETL & DWH with ease !

Training Schedules

S No	Time (IST, Mon - Fri)	Start Date
1	6 AM - 7 AM	Sep 2nd
2	10 AM - 11 AM	Aug 26th
3	6 PM - 7 PM	Sep 8th
4	8 PM -9 PM	Sep 15th

Azure Data Engineer
With AI Contents:

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Module 1 : Microsoft SQL (TSQL)

Ch 1: SQL SERVER INTRODUCTION

Database Introduction
Types of Databases
Need for & ETL, DWH
BI Implementations
SQL Server Advantages
Version, Editions of MSSQL
Data Analyst Job Roles

Ch 2: SQL SERVER INSTALLATIONS

SQL Server 2019, 2017
SSMS Tools Installation
Database Engine (OLTP)
SCM, Configuration Tools
Instance Types, Uses
Authentication Modes
Collation, File Stream

Ch 3: SQL BASICS – 1

Need for Databases, Tables
Need for SQL Commands
DDL, DML & DQL Statements
Database Creation @ GUI
Data Operations @ GUI
Session ID, SQL Context
DB, Tables, Data @ SQL

Ch 4: SQL BASICS – 2

DDL Variants in MSSQL
DML Variants in MSSQL
INSERT & INSERT INTO
SELECT & SELECT INTO
Basic Operators in SQL
Special Operators in MSSQL
ALTER, ADD, TRUNCATE, DROP

Ch 5: Data Imports, Schemas

Data Imports with Excel
ORDER BY & UNION
UNION ALL For Sorting Data
Creating, Using Schemas
Real-world Banking Database
Table Migrations @ Schemas
2 Part, 3 Part & 4 Part Naming

Ch 6 : Constraints, Index Basics

Need for Constraints, Keys
NULL, NOT NULL, UNIQUE
Primary Key & Foreign Key
RDBMS and ER Models
Identity Property, Default
Clustered Index, Primary Key
Non Clustered Index, Unique

Ch 7: Joins & Views Basics

JOINS: Purpose. Inner Joins
Left / Right / Full Outer Joins
Cross Joins, Query Tuning
Creating & Using Views
DML, SELECT with Views
RLS : WITH CHECK OPTION
System Views & Metadata

Ch 8: Functions(UDF), Data Types

Using Functions in MSSQL
Scalar Value Functions
Inline & Multiline Functions
Date & Time Functions
String, Aggregate Functions
Data Types : Integer, Char, Bit
SQL Variant, Timestamp, Date

Ch 9: Stored Procedures,Models

Stored Procedures & Usage
Creating, Testing Procedures
Encryption, Deferred Names
SPs for Validations, Analysis
System SPs, Recompilation
Normal Forms & Types
Data Models, Self-References

Ch 10: Triggers, Temp Tables

Need for Triggers
DDL & DML Triggers
Using Memory Tables
Data Replication, Automation
Local & Global Temp Tables
Testing & Using Temp Tables
SELECT .. INTO & Bulk Loads

Ch 11: DB Architecture, Locks

Planning VLDBs : Files, Sizing
Filegroups, Extents & Types
Log Files : VLF, Mini LSN
Table Location, Performance
Schemas, Transfer, Synonyms
Transactions Types, Lock Hint
Query Blocking Scenarios

Ch 12 : Cursors & CTEs, Links

Cursors : Realtime Use
Fetch & Access Cursor Rows
CTEs for SELECT, DML
CTEs: Scenarios & Tuning
Linked Servers, Remote Joins
Linked Servers: MSDTC, RPC
Tuning Remote Queries

Ch 13: Merge, Upsert & Rank

Need for Merge in ETL
Incremental Loads with SQL
MERGE and RANK Functions
Window Functions, Partition
Identify, Remove Duplicates

Ch 14: Grouping & Cube

Group By & HAVING
Cube, Rollup & Grouping
Joins with Group By
3 Table, 4 Table Joins
Query Execution Order

Ch 15: Self Joins, Excel Analysis

Self Joins & Self References
UNION, UNION ALL
Sub Queries with Joins
IIF, CASE, EXISTS Statements
Excel Analytics, Pivot Reports

Module 2: Azure Data Engineer

Ch 1: ETL, DWH Introduction

Database Introduction Data Warehouse (DWH)
Data Engineering Work Flow
Cloud Concepts: IaaS, PaaS
SaaS & Azure Cloud Concepts
Azure Resources & Groups
Storage, ETL, IoT Resources

Ch 2: Azure Intro, Azure SQL

Azure SQL Server, SQL DBA
Azure SQL Database (OLTP)
Azure SQL Pool (DWH)
Connections from SSMS Tool
Connections from ADS Tool
Pause / Resume SQL Pool
Source Data Configurations

Ch 3: Azure Synapse (DWH)

Synapse Pool Architecture
Control Node, Compute Node
DMS & Partitioned Tables
Creating Tables with TSQL
Distributions: RR, Hash, Repl
Big Data Loads with TQL
Important DMFs & DMVs

Ch 4: Azure Data Factory (ADF)

Need for ADF & Pipelines
Linked Services & IRs
Datasets, Pipelines, Triggers
Copy Data Activity & CDT
Data Loads Pipelines, DTUs
Pipeline Monitoring, Edits

Ch 5: ADF Incremental Loads – 1

File Incremental Loads
Storage Account, Data Lake
Binary Copy, Schema Drift
Staging Concept in ADF
DOCP, Logging & Consistency
Polybase Concept & Tuning

Ch 6: ADF Incremental Loads – 2

Implement SCD with ADF
Self-Hosted IR: Realtime Use
On-premise Data: Incr Loads
Copy Method: Upsert, Keys
Staging & ADF Optimizations
Pipeline Runs, Activity IDs

Ch 7: ADF Data Flow – 1

Data Flow Transformations
Spark Clusters for Debugging
Optimized Clusters, Preview
Conditional Split, SELECT
Sort, Union Transformations
Pipelines with Data Flow

Ch 8: ADF Data Flow – 2

Working with Multiple Tables
Join Transform, Broadcast
Row Filters, Column Filters
Surrogate Keys, Derived Cols
ETL Loads Dates, Sink Options
Aggregated Data Loads

Ch 9: ADF Data Flow – 3

Pivot Transformation
Group By & Pivot Keys
Column Pattern, Deduplicate
Lookup, Cached Lookup
Tuning Transformations
Tuning Data Flow, Spark

Ch 10: Synapse Analytics – 1

Azure Synapse Analytics
Dedicated SQL Pools
TSQL: Stored Procedures
Synapse Pipelines, Tuning
SP Activity in Pipelines, Jobs
Comparing ADF & Synapse

Ch 11: Synapse Analytics – 2

Serverless Pools in Synapse
TSQL Scripts with Serverless
ADLS Data Imports & ELT
Synapse Aggregation, Analytics
Synapse Optimizations
Synapse Security & Logins

Ch 12: Synapse Analytics – 3

Apache Spark Pool & Usage
Synapse Analytics with Pools
PySpark Staging, Aggregations
Spark Queries & Python ETL
Python Notebooks, Pipelines
Integrating Python with DWH

Ch 13: Parameters, SCD & ETL

ADF Templates in Realtime
Table Incremental Loads
Control Tables, Watermarks
Pipeline Parameters, SPs
Dynamic Data Sets, SCD

Ch 14: CDC @ ETL, ELT & Tuning

Using CDC in ADF
Control Tables (CT): Upserts
Handling Inserts, Updates
SCD Type 1 & Type 2
ADF, Synapse: Limitations

Ch 15: Azure Intro & Storage

Storage, ETL, IoT Resources
Azure Storage Components
Azure Storage Account, HNS
Azure Data Lake Storage
Azure Storage Explorer Tool
Storage Explorer Config
Storage Account Properties

Ch 16: Azure Storage Operations

BLOB Storage: Containers
Storage Browser, Explorer
File & Folder Uploads, Edits
Azure Tables: Row Key
Partition Key, Timestamp
Use Cases of BLOB Storage
Use Cases of Azure Tables

17: Azure Storage Security

Realtime use of Keys
Access Keys & Admin Access
SAS Keys Generation, Ips
Creating, Using Entra Users
Azure AD Users, Groups
IAM & RBAC with Entra Users
ACLs and ADLS Security

Ch 18: Azure SQL DB Migrations

On-Premise SQL DB bacpac
Azure SQL Deployment
Azure Storage from SSMS
Azure SQL DB Migration
Migration Verifications
Testing Migrations in SQL

Ch 19: Azure Stream Analytics

Azure IoT Hubs & Devices
APIs with Connection Strings
Azure Steam Analytic Jobs
Inputs, Outputs, SAQL Query
LIVE Feed: JSON, AVRO Files
Watermark & LIVE Stats

Ch 20: Azure Stream Analytics

Azure IoT Hubs & Devices
APIs with Connection Strings
Azure Steam Analytic Jobs
Inputs, Outputs, SAQL Query
LIVE Feed: JSON, AVRO Files
Watermark & LIVE Stats

Ch 21: Azure Key Vaults, Alerts

Azure Encryptions @ REST
Azure Key Vaults & Keys
SMK & CMK Encryptions
Azure Metrics: Ingress
Egress, E2E Latency Issues
Performance Tuning Options

Ch 22: Azure Storage Optimization

BLOB Types & Content Types
Hot, Cool, Cold, Archive Types
Creating, Using Access Policies
Immutable Storage, Rotation
Containerization, Indexing
Replication: LRS, ZRS, RA-GRS

Ch 23: Azure Pricing, Functions

Azure Logic Apps: Usage
Log Apps Usage in ETL
Snapshots, Azure Functions
Azure Functions Realtime Use
ETL & DWH with Functions
Azure Resource Pricing

Ch 24: Azure Big Data & Spark

Azure Big Data & Spark
Azure ETL & DWH Databases
Azure Spark, HIVE Metastore
Azure Databricks Service
Spark Cluster (Personal)
Unity Catalog & Azure VM

Ch 25: Spark Cluster Operations

DBFS: Flat File Imports
Table Conversions using GUI
Spark Clusters: Table Creations
Basic Transformations in Spark
SQL Notebooks: Creation
Default DB Queries, Cloning

Ch 26: Python & PySpark, ETL

Python Fundamentals
Python Data frames: ETL
Python for Big Data, Pandas
Python Notebooks, Views
Aggregated Loads to Spark
Spark DB Creations, Tables

Ch 27: PySpark & ADLS, Widgets

Creating Spark Databases
Spark Tables, Catalog Info
PySpark with ADLS Storage
Using Widgets for ADLS Keys
PySpark Variables & Widgets
Using Variables in Functions
Spark SQL with Control Text
Using Variables in Spark SQL

Ch 28: ADB Jobs, Delta Tables

Azure Databrick Jobs
Azure Workflows & Tasks
Notebook Schedule Options
Continuous Jobs, Notifications
Delta Tables & Data Cleansing
SCD (Merge Into), Contact, etc.
Creating, Using Data frames
Multi Data frame Joins

Ch 29: Scala Notebooks & ETL

Scala Notebooks: Purpose
Aggregated Data Loads
Incremental Data Loads
Widgets & Jobs with Scala
Python Versus Scala
Converting Python to Scala
JVM Benefits, SQL DB Conn”
SQL DB Loads with Scala

Ch 30: Databricks Architecture

Azure Databricks Services
Cluster Components & DBFS
RDD, DAG, Photon, Spotlight
Spark Partitioned Tables
Cluster Manager: Spark Jobs
Databricks Runtime (DBR)
Databricks Security
Workspace Security
Notebook & Job Security

Ch 31: Medallion Architecture

Medallion Architecture in ETL
DWH Data Loads & Incr Loads
Bronze, Silver & Gold Data
Processing Raw Data Files
Data Cleansing, Formatting
Aggregation Advantages
DBES & Node Architecture
Unity Catalog Concept
LUNs and Unity Catalog

Ch 32: Delta LIVE Tables (DLT)

Creating Delta LIVE Tables
DLT Pipelines in ETL, DWH
Automated Incr Loads
Control Tables, Timestamp
SCD Type 1 with DLT
SCD Type 2 with DLT
Automated Merge Into Stmt
Delta Tables Vs DLT
Merge Into Vs DLT Pipeline

Module 3: Azure AI & CoPilot

Ch 1 : Fundamental AI Concepts

AI: Artificial Intelligence
Real-time Implementation
Understand Computer Vision
Understand Natural Language Processing
Document Intelligence and Knowledge Mining
Understand Generative AI
Challenges and Risks with AI
Understand Responsible AI

Ch 2: Fundamentals of Machine Learning

Machine Learning Introduction
Machine Learning Components
Types of Machine Learning
Regression, Binary Classification; Multiclass Classification
Clustering, Deep Learning
Azure Machine Learning

Ch 3 : Fundamentals of Azure AI services

AI Services on Azure platform
Create Azure AI Service Resources
Use Azure AI services
Understand Authentication for Azure AI services
Exercise – Explore Azure AI Services

Ch 4 : Computer Vision

Images and image processing
Machine learning for computer vision
Azure AI Vision
Exercise – Analyze images in Vision Studio

Ch 5 : Natural Language Processing

Understand Text Analytics
Text Analysis in Azure
Exercise – Analyze text with Language Studio

Ch 6 : Document Intelligence and Knowledge Mining

Introduction to Document Intelligence
Knowledge Mining
Explore capabilities of document intelligence
Receipt Analysis on Azure
Exercise – Extract from data in Document Intelligence Studio

Ch 7 : Generative AI

What is generative AI?
What are language models?
Using language models
What are copilots?
Considerations for Copilot prompts
Extending and developing copilots
Exercise – Explore Microsoft Copilot

Ch 8 : Generative AI in Azure

Generative AI – Capabilities within AI in Azure
Azure Implementation of Gen AI
Processing Images, Codes and more

Ch 9 : AI 900 Exam Guidance

Describe Artificial Intelligence workloads and considerations
Describe fundamental principles of machine learning on Azure
Describe features of computer vision workloads on Azure
Describe features of Natural Language Processing (NLP) workloads on Azure
Describe features of generative AI workloads on Azure

Ch 10 : Azure AI with Data Analytics – 1

Implementing AI in Cloud
Co-Pilot Concepts in Big Data
AI with Azure
AI with Azure SQL Database
Automated Query Tuning Concepts (OLTP)

Ch 11 : Azure AI with Data Analytics – 2

AI with Power BI
CoPilot with Power BI – Power Query
CoPilot with Power BI – Cloud
CoPilot with Power BI – DAX

Ch 12 : Azure AI with Azure Data Engineering – 3

AI with Azure Storage Account
ADLS Concepts and AI Implementations
AI Search Service with ADLS
Text Data Handling with AI

SQL SCHOOL

24x7 LIVE Online Server (Lab) with Real-time Databases.
Course includes ONE Real-time Project.

Azure Data Engineer With AI Training FAQ's

What is Azure Data Engineer with AI Job Role?

An Azure Data Engineer with AI specialization is a modern data professional responsible for designing, building, and maintaining end-to-end data solutions in Azure that integrate AI and machine learning capabilities. This role combines traditional data engineering tasks like data ingestion, transformation, storage, and performance tuning with advanced AI integrations such as Azure Cognitive Services, OpenAI APIs, Azure ML, and SynapseML to support intelligent analytics and predictive applications.

What are the Job Roles of an Azure Data Engineer with AI?

💼 Top Job Roles:

1️⃣ Build and manage scalable data pipelines using Azure Data Factory, Synapse & Data Lake
2️⃣ Integrate AI APIs (OpenAI, Azure Cognitive Services, Azure ML) into data workflows
3️⃣ Implement data models, warehouses, and real-time data solutions
4️⃣ Develop intelligent dashboards, semantic layers, and custom ML integrations
5️⃣ Ensure security, governance, and compliance of AI-powered data systems
6️⃣ Collaborate with data scientists, BI developers, and business stakeholders and more..!

What does our Azure Data Engineer with AI Training course contain?

The course is carefully curated with below module:
👉🏻Module 1: Microsoft SQL (TSQL)
👉🏻Module 2: Azure Data Engineer with AI
👉🏻Module 3: Azure AI, Co-Pilot

Who can join this course?

Freshers interested in cloud BI and analytics
SQL/BI professionals expanding to AWS BI tools
ETL developers moving to AWS cloud solutions
Data analysts aiming for AWS BI certifications
Anyone looking to build enterprise BI solutions on AWS

No prior coding experience is required. All concepts are taught from scratch

What training modes are available?

Option 1: LIVE Online Training (100% Interactive, step by step, assignments)

Option 2: Self Paced Videos (100% practical, step by step with concept wise assignments)

You may choose any one of these options, same curriculum!

I (Trainer) shall be available for doubts and clarifications, assignment check and review.

Why should I choose SQL School for Azure Data Engineer With AI training?

👉🏻 Every session is Practical, Step by Step with Concept wise FAQs !!

👉🏻 100% results with on-time practice. Daily Tasks for every session.

👉🏻 Concept wise tasks be submitted before next class for Job Waiters / Starters.

👉🏻 Concept wise tasks due for submission by Weekends for Working Professionals.

Why Choose SQL School

100% Real-Time and Practical
ISO 9001:2008 Certified
Concept wise FAQs
TWO Real-time Case Studies, One Project
Weekly Mock Interviews
24/7 LIVE Server Access
Realtime Project FAQs
Course Completion Certificate
Placement Assistance
Job Support
Realtime Project Solution
MS Certification Guidance